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Found 17 papers, 0 papers with code
Event-to-Video Conversion for Overhead Object Detection
In this paper, we investigate the viability of event streams for overhead object detection.
LCANets++: Robust Audio Classification using Multi-layer Neural Networks with Lateral Competition
Audio classification aims at recognizing audio signals, including speech commands or sound events.
Implementing and Benchmarking the Locally Competitive Algorithm on the Loihi 2 Neuromorphic Processor
Neuromorphic processors have garnered considerable interest in recent years for their potential in energy-efficient and high-speed computing.
Sampling binary sparse coding QUBO models using a spiking neuromorphic processor
We demonstrate neuromorphic computing is suitable for sampling low energy solutions of binary sparse coding QUBO models, and although Loihi 1 is capable of sampling very sparse solutions of the QUBO models, there needs to be improvement in the implementation in order to be competitive with simulated annealing.
Dictionary Learning with Accumulator Neurons
The Locally Competitive Algorithm (LCA) uses local competition between non-spiking leaky integrator neurons to infer sparse representations, allowing for potentially real-time execution on massively parallel neuromorphic architectures such as Intel's Loihi processor.
Prediction and compression of lattice QCD data using machine learning algorithms on quantum annealer
In the compression algorithm, we define a mapping from lattice QCD data of floating-point numbers to the binary coefficients that closely reconstruct the input data from a set of basis vectors.
Uncovering Universal Features: How Adversarial Training Improves Adversarial Transferability
Adversarial examples for neural networks are known to be transferable: examples optimized to be misclassified by a “source” network are often misclassified by other “destination” networks.
A Little Robustness Goes a Long Way: Leveraging Robust Features for Targeted Transfer Attacks
Adversarial examples for neural network image classifiers are known to be transferable: examples optimized to be misclassified by a source classifier are often misclassified as well by classifiers with different architectures.
Adversarial Perturbations Are Not So Weird: Entanglement of Robust and Non-Robust Features in Neural Network Classifiers
The results we present in this paper provide new insight into the nature of the non-robust features responsible for adversarial vulnerability of neural network classifiers.
The Selectivity and Competition of the Mind's Eye in Visual Perception
Research has shown that neurons within the brain are selective to certain stimuli.
It's Hard for Neural Networks To Learn the Game of Life
To investigate how weight initializations affect performance, we examine small convolutional networks that are trained to predict n steps of the two-dimensional cellular automaton Conway's Game of Life, the update rules of which can be implemented efficiently in a 2n+1 layer convolutional network.
A regression algorithm for accelerated lattice QCD that exploits sparse inference on the D-Wave quantum annealer
We propose a regression algorithm that utilizes a learned dictionary optimized for sparse inference on a D-Wave quantum annealer.
Image classification using quantum inference on the D-Wave 2X
To establish a benchmark for classification performance on this reduced dimensional data set, we used an AlexNet-like architecture implemented in TensorFlow, obtaining a classification score of $94. 54 \pm 0. 7 \%$.
Classifiers Based on Deep Sparse Coding Architectures are Robust to Deep Learning Transferable Examples
Although deep learning has shown great success in recent years, researchers have discovered a critical flaw where small, imperceptible changes in the input to the system can drastically change the output classification.
Phase Transitions in Image Denoising via Sparsely Coding Convolutional Neural Networks
Neural networks are analogous in many ways to spin glasses, systems which are known for their rich set of dynamics and equally complex phase diagrams.
Sparse Coding on Stereo Video for Object Detection
We show that replacing a typical supervised convolutional layer with an unsupervised sparse-coding layer within a DCNN allows for better performance on a car detection task when only a limited number of labeled training examples is available.
Replicating Kernels with a Short Stride Allows Sparse Reconstructions with Fewer Independent Kernels
We find, for example, that for 16x16-pixel receptive fields, using eight kernels and a stride of 2 leads to sparse reconstructions of comparable quality as using 512 kernels and a stride of 16 (the nonoverlapping case).
